Crowdfunding ETL Pipeline
Description:
Built an end-to-end ETL pipeline for crowdfunding campaign data, extracting from CSVs, transforming with Python, and loading into PostgreSQL. Demonstrates core skills in data engineering, schema design, and workflow automation.
Problem:
Crowdfunding campaign data is often scattered across multiple sources, making it difficult to analyze or report effectively.
Methods:
Extracted campaign data from multiple CSV files.
Transformed and cleaned the data using Python + Pandas.
Designed and implemented a PostgreSQL relational schema.
Loaded processed data into the database for analysis.
Tools: Python, PostgreSQL, ETL, Pandas
Results:
End-to-end ETL pipeline with clean, relational datasets.
ERD schema that enables structured analysis of campaign outcomes.
Business Impact:
This project shows how ETL pipelines streamline raw, messy data into actionable insights — a critical skill for building analytics workflows in real business settings.